class_docstr: This is the configuration class to store the configuration of a [`DetrModel`]. It is used to instantiate a DETR
  model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
  defaults will yield a similar configuration to that of the DETR
  [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      use_timm_backbone (`bool`, *optional*, defaults to `True`):
          Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
          API.
      backbone_config (`PretrainedConfig` or `dict`, *optional*):
          The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
          case it will default to `ResNetConfig()`.
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      num_queries (`int`, *optional*, defaults to 100):
          Number of object queries, i.e. detection slots. This is the maximal number of objects [`DetrModel`] can
          detect in a single image. For COCO, we recommend 100 queries.
      d_model (`int`, *optional*, defaults to 256):
          Dimension of the layers.
      encoder_layers (`int`, *optional*, defaults to 6):
          Number of encoder layers.
      decoder_layers (`int`, *optional*, defaults to 6):
          Number of decoder layers.
      encoder_attention_heads (`int`, *optional*, defaults to 8):
          Number of attention heads for each attention layer in the Transformer encoder.
      decoder_attention_heads (`int`, *optional*, defaults to 8):
          Number of attention heads for each attention layer in the Transformer decoder.
      decoder_ffn_dim (`int`, *optional*, defaults to 2048):
          Dimension of the "intermediate" (often named feed-forward) layer in decoder.
      encoder_ffn_dim (`int`, *optional*, defaults to 2048):
          Dimension of the "intermediate" (often named feed-forward) layer in decoder.
      activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      dropout (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      activation_dropout (`float`, *optional*, defaults to 0.0):
          The dropout ratio for activations inside the fully connected layer.
      init_std (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      init_xavier_std (`float`, *optional*, defaults to 1):
          The scaling factor used for the Xavier initialization gain in the HM Attention map module.
      encoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      decoder_layerdrop (`float`, *optional*, defaults to 0.0):
          The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
          for more details.
      auxiliary_loss (`bool`, *optional*, defaults to `False`):
          Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
      position_embedding_type (`str`, *optional*, defaults to `"sine"`):
          Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
      backbone (`str`, *optional*, defaults to `"resnet50"`):
          Name of convolutional backbone to use in case `use_timm_backbone` = `True`. Supports any convolutional
          backbone from the timm package. For a list of all available models, see [this
          page](https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model).
      use_pretrained_backbone (`bool`, *optional*, defaults to `True`):
          Whether to use pretrained weights for the backbone. Only supported when `use_timm_backbone` = `True`.
      dilation (`bool`, *optional*, defaults to `False`):
          Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
          `use_timm_backbone` = `True`.
      class_cost (`float`, *optional*, defaults to 1):
          Relative weight of the classification error in the Hungarian matching cost.
      bbox_cost (`float`, *optional*, defaults to 5):
          Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
      giou_cost (`float`, *optional*, defaults to 2):
          Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
      mask_loss_coefficient (`float`, *optional*, defaults to 1):
          Relative weight of the Focal loss in the panoptic segmentation loss.
      dice_loss_coefficient (`float`, *optional*, defaults to 1):
          Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
      bbox_loss_coefficient (`float`, *optional*, defaults to 5):
          Relative weight of the L1 bounding box loss in the object detection loss.
      giou_loss_coefficient (`float`, *optional*, defaults to 2):
          Relative weight of the generalized IoU loss in the object detection loss.
      eos_coefficient (`float`, *optional*, defaults to 0.1):
          Relative classification weight of the 'no-object' class in the object detection loss.
  
  Examples:
  
  ```python
  >>> from transformers import DetrConfig, DetrModel
  
  >>> # Initializing a DETR facebook/detr-resnet-50 style configuration
  >>> configuration = DetrConfig()
  
  >>> # Initializing a model (with random weights) from the facebook/detr-resnet-50 style configuration
  >>> model = DetrModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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docstr: Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration.
  
  Args:
      backbone_config ([`PretrainedConfig`]):
          The backbone configuration.
  Returns:
      [`DetrConfig`]: An instance of a configuration object
docstr_tok: ["Instantiate","a","[","`","DetrConfig","`","]","(","or","a","derived","class",")","from","a","pre-trained","backbone","model","configuration",".","Args",":","backbone_config","(","[","`","PretrainedConfig","`","]",")",":","The","backbone","configuration",".","Returns",":","[","`","DetrConfig","`","]",":","An","instance","of","a","configuration","object"]
code: def from_backbone_config(
      cls, backbone_config: PretrainedConfig, **kwargs
  ):
      """Instantiate a [`DetrConfig`] (or a derived class) from a pre-trained backbone model configuration.
  
      Args:
          backbone_config ([`PretrainedConfig`]):
              The backbone configuration.
      Returns:
          [`DetrConfig`]: An instance of a configuration object
      """
      return cls(backbone_config=backbone_config, **kwargs)
  
file_path: transformers/src/transformers/models/detr/configuration_detr.py
imports: from collections import OrderedDict
  from typing import Mapping
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
label_desc: Use image node_id 4 for calling the DetrConfig obj's underlying member method code with example usage: obj.from_backbone_config(cls, backbone_config) and returns: cls